 from the Walt Disney World Swan and Dolphin Resort in Orlando, Florida. It's theCUBE. Covering Splunk.com 2016. Brought to you by Splunk. Now, here are your hosts, John Furrier and John Walls. We are here live at our lunch.com for 2016. It's the seventh annual conference for their customers and company event. This is theCUBE, SiliconANGLE's flagship program. We brought to you the events that struck the scene of the noise. In our seventh year, I'm John Furrier with my co-host, John Walls. Seven years. Seven. theCUBE and Splunk. And we have Josh Rodgers with us who's the CEO of SingSort who's been in the business for almost 50 years. So we're covering quite a time span here. The combined knowledge here is 150 years. We should splunk on the chart. And almost your first year as CEO. So congratulations on that. I'm sure it's been quite an eventful year for you in many respects. For sure. Yeah, first off, let's just, I'm kind of curious, 50 years. Yeah. Lots gone on then, right? That's about, you know, it's in dog years. I don't know how many that is. But just the differences you think of how your environments evolved and how the companies had to evolve with it. Yeah, sure. Well, I mean, you see the rapid transformation of hardware and software and what becomes possible. And so people are just tackling much bigger issues. And what we're trying to do is make sure that we take our original DNA and provide unique value. And obviously big data is a big focus for us now. And that's where we think continues to be relevant. It's just we need to deliver it to new systems. And so that's what COVID's done. So what are you doing with Splunk now? So this started about two and a half, three years ago. We actually met Splunk at another big data conference and progress in the enterprise. And the large enterprises or their customers, not surprisingly, wanted to move mainframe log data into Splunk environments to be able to complete the view of their IT operations and security customers. And Splunk didn't have a great sense of how to do that. Mainframe has been around for a long time. It stores data differently, manages data differently, has a different cost model. They're in a big shop too. The mainframes are in all... Still leverage the mainframe. And so we partnered with Splunk to take the picture of operations, their IT service intelligence and their security. It's funny how the world becomes full circle. The word data processing was a term back then. An I.O. supercomputer and there's no business process that's been baked into these applications makes it very difficult for these large financial institutions and telcos to move off of that environment. Having said that, to contribute the spend and the workloads such that they can put various workloads that are leveraging Splunk, they're leveraging Hadoop. And so what we see happening is that customers with mainframes are facing a challenge of, how do I take the core data assets that are being created through my transactional workloads next generation analytics? You see a lot of big data vendors that have mainframe expertise. And you see mainframe organizations that vendors that actually don't have a lot of big data. Do they initially have the incentive to help kind of bridge that gap? We call that challenge the big iron to big data challenge. And it's imperative for these large organizations to incorporate their core transactional data in these new systems that they need help to do. What are the key drivers, Josh? Take a minute to talk about the key drivers. Is it because the apps are single threaded or mainframe specific? Or is it because now you can add Splunk? It's some capabilities. So if you think about what's happening with the big data environments, I can now ask bigger questions across broader sets of data from status. So if I'm bringing in IoT data and I'm just sensitive data into this open source platform or Splunk takes a significant amount of knowledge and SingSort uniquely provides that set of expertise and technology. We call that problem big iron to Wikibon who does the best sizing from a market perspective of big data to help us size that sub-market market. What size are we talking about now, how big? So if you think about the big data software market, Wikibon sizes at about 20%. So a very big infrastructure software market is about $20 billion. It's actually about twice the size of the big data market. It's an intersection of the two. And Wikibon says that that intersection is about a billion dollars today. That kind of integration of mainframe and big data, 25%. So actually growing a little, and that's where frankly we intend to lead the market. And we're already leading market with products like Ironstream and DMXH in these cases we're delivering with customers. I mean it's like the classic line extension concept where you have a position which you're dominating in and then you go into areas where you can create value off the data and the asset. That's right. And then that's where the analytics comes in. Absolutely, and it turns out it's a hard problem. You know we had an insurance customer that was looking to drive compliance around application developers where application data and test cycles and they wanted to make sure they weren't violating any sort of PII. Ironstream and Splunk were able to build their own compliance app that could show these are how your policies. And the customer should have to buy much into it. You can pick and choose where Splunk makes sense to them vis-a-vis the mainframe or assets for sync stores. That's right. Yeah, they were already using Splunk. They knew they had certain log records that showed who was using which data sets. They just wanted to grab those log records. There's so much movement as you know. Merger, acquisition. You've been busy. Yes. Yeah, based in the UK called Kajido. They focus on IDMS and DB2 performance tools. IDMS is a facto standard relational database for mainframe. Performance tools are helpful for our customers. So we have a large set of mainframe customers that are always looking to control costs. And one of the ways to do that is to optimize performance and efficiency, particularly for your relational data workload. Having IDMS and DB2 skills is incredibly valuable for our organization as we deliver, seek to deliver enhancements to our big R&B data strategy. If, you know. What's the combination create? All those transactions that get processed on the mainframe, they get stored in DB2. And so if I'm running, if I have a Hadoop cluster and I'm looking to figure out how do I move all of my DB2 tables into Hadoop, having some DB2 expertise in house is incredibly helpful. It also turns out that DB2 throws off a lot of logs. You can understand a lot about what's happening in your business based on the logs of the DB2. If I have expertise in understanding logs, I can make those available to solutions like IronStream and Splunk to better run that environment. So these acquisitions, this is our third in helping us deepen our expertise to solve this big R&B data challenge. But they also give us additional products we can sell to our traditional customers. But more than connecting the data, so big R&B data makes sense if I buy that 100%. But the nuance here is the value of the data that's moving between the environments. You're enabling big data with the impact of the customer. So we have a insurance customer on the Hadoop side that offers discounts for use a piece of hardware that tracks your driving history. And that IoT data stream comes back to a Hadoop cluster. And to actually produce the models that say, how much should I charge you? How much should the discount be? They need your claims history. They need your premium history. That is all stored as DB2 tables in the mainframe. So if they can't take that mainframe data asset and combine it with this new IoT data asset, they can't actually drive the business outcome they're looking for. And that's the connection that we make. So the next step for you guys, what's next? You connect all the environments. You like switching one, right? I mean, is that the strategy? We're very focused on building reach with our big data parts. And Splunk is a perfect example. So we've had Iron Stream in the market for about coming up on two years. And it moves the broadest set up into Splunk. And that's great. And that's a great capability. But Splunk continues to advance its offering. And you've seen these application add-ons that come out to come to market with enterprise security and IT service intelligence. Well, if you're an Iron Stream customer and you're a Splunk customer and you wanted to understand the health of various services in your environment, you can leverage Splunk's, they call ITSY, IT Service Intelligence app. Wouldn't it be great if someone was out there to map all the mainframe logs into the ITSY data model? And that's what we've done. So this, we actually are announcing this week support for ITSY. We've already announced support for enterprise security. And so as an Iron Stream customer, not only do you get the ability to move this log data in, but you get the ability to make it immediately useful in the context of the capabilities that Splunk's already delivered to. So that's key to our strategy, is to deliver additional enhancements that deepen our relationship and the value that customers can get from their investments in big data technology. And to find and to explain to the people watching, how do they engage with ThinkSort? Why should they buy you? Why should I work with you and when? So, look, it's going to have to make significant investments in big data technologies to drive the analytics they need to stay competitive. Investments, they're going to have to kind of address this big iron to big data challenge. And it's not easy. And there are not a lot of vendors that have both the technology and the expertise to help solve. ThinkSort is the leader in the big iron to big data market. We'll continue to be, and we'll continue to partner with our customers to solve a growing number of use cases to help them chart that path. Josh Rocher, CEO of ThinkSort. Big iron to big data. If you've got a mainframe, check out ThinkSort. Great to see you. You guys have been a CUBE alumni for so many years. I can't even count. And combined experience here is over 290 years. That sounds good. 1.167th annual user conference. All out the cloud. It's all about big data. It's all about user...